By Jean-Christophe Zufferey
This e-book demonstrates how bio-inspiration can result in totally self sufficient flying robots with out counting on exterior aids. such a lot present aerial robots fly in open skies, faraway from hindrances, and depend on exterior beacons, in most cases GPS, to localise and navigate. in spite of the fact that, those robots can not fly at low altitude or in restricted environments, and but this poses totally no trouble to bugs. certainly, flying bugs demonstrate effective flight keep watch over functions in complicated environments regardless of their constrained weight and comparatively tiny mind measurement. From sensor suite to manage suggestions, the literature on flying bugs is reviewed from an engineering point of view as a way to extract invaluable ideas which are then utilized to the synthesis of synthetic indoor flyers. man made evolution is usually utilised to look for replacement regulate structures and behaviors that fit the limitations of small flying robots. in particular, the elemental sensory modalities of bugs, imaginative and prescient, gyroscopes and airflow feel, are utilized to enhance navigation controllers for indoor flying robots. those robots are in a position to mapping sensor info onto actuator instructions in actual time to keep up altitude, stabilize the direction and stay away from hindrances. the main famous results of this novel strategy is a 10-gram microflyer in a position to totally self sustaining operation in an office-sized room utilizing fly-inspired imaginative and prescient, inertial and airspeed sensors. This ebook is meant for all these in autonomous robotics, in academia and undefined.
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Extra resources for Bio-inspired Flying Robots: Experimental Synthesis of Autonomous Indoor Flyers (Engineering Sciencs: Microtechnology)
Sample text
At the Swiss Federal Institute of Technology in Lausanne (EPFL), Floreano and Mattiussi [2001] have carried out experiments where a small wheeled robot evolved the ability to navigate in a randomly textured environment. The robot was equipped with a 1D camera composed of 16 pixels with a 36◦ FOV as its only sensor. Evolution could relatively quickly find functional neuromorphic controllers capable of navigating in the environment without hitting the walls, and this by using a very simple genetic encoding and fitness function.
The task of the agent was to navigate as far as possible in a corridor-like environment with a few perpendicular obstacles. Four photodetectors were brought together to compose two elementary motion detectors (see Chap. 3), one on each side of the agent. The simple sensory-motor architecture was inspired from Braitenberg [1984]. Despite their minimalist sensory system, the autonomous agents successfully adapted to the task during artificial evolution. The best evolved individuals had a sensor orientation and a sensory-motor coupling suitable for collision avoidance.
Evolution could relatively quickly find functional neuromorphic controllers capable of navigating in the environment without hitting the walls, and this by using a very simple genetic encoding and fitness function. , 1997), but the chosen tasks (pursuit and evasion) are not directly related to the ones tackled in this book. , 1994]. © 2008, First edition, EPFL Press 30 Conclusion raw vision. The visual input was simply preprocessed with a spatial highpass filter before feeding a general purpose neural network and the sensory morphology was not concurrently evolved with the controller architecture.